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In this 53-minute lecture, UC Berkeley professor Bin Yu presents a framework for veridical data science as a foundation for trustworthy AI. Explore the theoretical aspects of creating reliable and transparent artificial intelligence systems through data-driven approaches. Learn how rigorous statistical methods and responsible practices can help build AI systems that produce accurate, interpretable, and ethically sound results. Part of the Theoretical Aspects of Trustworthy AI series from the Simons Institute.
